The Library of Congress, Dewey Decimal, and Universal Decimal Classification Systems are Incomplete and Unsystematic
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Objective – To determine the extent to which knowledge is currently addressed by the Library of Congress (LCC), Dewey Decimal (DDC), and Universal Decimal (UDC) classification systems.
 
 Design – Comparative analysis of the LCC, DDC, and UDC systems using Zin’s 10 Pillars of Knowledge.
 
 Setting – The Faculty of Philosophy and Science at a Brazilian university.
 
 Subjects – Forty one subject-related classes and 386 subclasses from the first two levels of the LCC, DDC, and UDC systems. 
 Methods – To evaluate the LCC, DDC, and UDC systems, the researchers employed the 10 Pillars of Knowledge, a “hierarchical knowledge tree” developed by the lead author of this study (p. 878). According to the authors, the 10 Pillars of Knowledge seek to illustrate relationships between fields of knowledge while capturing their breadth. The first level of the Pillars consists of the following categories: Knowledge, Supernatural, Matter and Energy, Space and Earth, Nonhuman Organizations, Body and Mind, Society, Thought and Art, Technology, and History. Each of the 10 Pillars is further subdivided, resulting in a four level hierarchical structure of 76 categories. Of the 76 categories, 55 are unique subject areas. A selection of subject-based classes and subclasses from the first two levels of the LCC, DDC, and UDC systems were then mapped to the relevant subclasses within the Pillars. Analysis was limited to the first two levels of LCC, DDC, and UDC, except for the LCC categories of BF and BL where further subclasses were analyzed. Classes or subclasses in LCC, DDC, or UDC that were not subject based (for example, those based on publication type) were excluded from the study. In total, 41 main classes and 386 subclasses from LLC, DDC, and UDC were categorized using the 10 Pillars.
 
 Main Results – The LLC, DDC, and UDC systems were deemed to be complete and systematic in their coverage of only three of the 10 Pillars: Matter and Energy, Thought and Art, and History. This means that there was at least one class or subclass in each of the three systems that corresponded to the subclasses in these pillars. The remaining seven pillars were only partially covered by the three systems to varying degrees. For example, the coverage of religion in LCC and DDC show evidence of a bias towards Christianity and incomplete coverage of other faiths. In addition to the lack of completeness in terms of subject coverage, the researchers found inconsistencies and problems with how relationships between subjects were illustrated by the systems. For example, botany should be a subclass of biology, but the subjects occupy the same level in the LCC, DDC, and UDC systems. Researchers also noted cases where subclasses on the same level were not mutually exclusive e.g., the BR (Christianity) and BS (The Bible) subclasses in LCC. Overall, LLC performed slightly better than DDC or UDC, covering 47 of the 55 unique subject categories in the 10 Pillars. It was followed by UDC with 44 out of 55, and DDC with 43 out of 55. Some of the 55 unique subject categories in the 10 Pillars system were not represented by any of the systems: 3 subclasses under Society (Society at Large – Area Based, Social Groups – Age, and Social Groups – Ethnicity), 2 under Technology (Technologies – Materials and Technologies – Processes), and 1 under Foundations (Methodology).
 
 Conclusion – The researchers conclude that none of the three major classification systems analyzed provides complete and systematic coverage of the world of knowledge, and call for the library community to move to new systems, such as the 10 Pillars of Knowledge.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,350 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle